Skip to content

Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease

Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease

Status
Not yet recruiting
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06695273
Acronym
DeepCHD Plus
Enrollment
1570
Registered
2024-11-19
Start date
2025-01-31
Completion date
2025-05-31
Last updated
2024-11-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Coronary Heart Disease (CHD)

Brief summary

To determine whether an integrated retinal AI decision support can improve predictive accuracy of coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided prediction of CHD compared to clinical prediction by physicians (e.g., usingPCEs), both using clinical intuition as baseline.

Detailed description

This is a randomized controlled trial (RCT) evaluating the effectiveness of an AI-based decision support tool in CHD risk prediction and decision making by physicians. Prospective cohort study participant cases will be randomly assigned to either guideline group (e.g., PCEs) or AI group after baseline assessment (clinical intuition): There are three settings: (1) Clinical Intuition (baseline assessment) Physicians' make decision about prevention strategy initiation (e.g., statin initiation) without any external assistance. Assessment relies solely on the physician's clinical judgment and experience. (2) Guideline-Based Group (Guideline Group) Physicians use a PCE table to calculate the 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making. (3) AI-Assisted Group (AI Group) Physicians receive CHD probability estimates from an AI model based on retinal photographs. The AI tool provides individualized obstructive CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk. Primary Objective To evaluate whether AI-guided decision support could improves diagnostic accuracy of CHD to a greater extent than standard clinical assessments, both compared to clinical intuition. The accuracy could be assessed by the extent of prevention initiation (e.g., prescribing statins) corresponding with actual CHD outcomes observed. Secondary Objective To assess whether AI-guided decision support reduces the time required to complete CHD assessments and decision making. Participants, Readers and Randomization: Participants: Participants in prospective cohort studies, with 10-year follow up. Readers: Physicians performing evaluations of CHD probability and make primary prevention recommendations. Randomization: Participants will be randomized into one of the groups (PCEs or AI) after clinical assessment at baseline using block randomization to ensure balanced group sizes.

Interventions

DIAGNOSTIC_TESTAI-derived probability of coronary heart disease.

Physician readers will be assisted with AI-derived probability of coronary heart disease. The AI tool provides individualized obstructive CHD probabilities and diagnosis, leveraging retinal biomarkers associated with cardiovascular risk.

DIAGNOSTIC_TESTPCEs derived ASCVD risk

Physicians use a PCEs to calculate the probability of 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making.

Sponsors

Tsinghua University
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
SINGLE (Outcomes Assessor)

Eligibility

Sex/Gender
ALL
Age
40 Years to 75 Years
Healthy volunteers
Yes

Inclusion criteria

* Individuals without uncontrolled vascular risk factors * Age range: 40-75 years old * Can accept and cooperate with the examination and potential follow-up work after being selected for clinical trials

Exclusion criteria

* Severe lung disease and cancer or surgery patients * Statin user or pre-existing cardiovascular disease * Individuals with severe liver and kidney dysfunction and electrolyte imbalance

Design outcomes

Primary

MeasureTime frameDescription
AccuracyThrough study completion, an average of 1 weekTo evaluate whether AI-guided decision support could improves diagnostic accuracy of CHD to a greater extent than standard clinical assessments, both compared to clinical intuition. The accuracy could be assessed by the degree to which prevention initiation (e.g., prescribing statins) align with actual CHD outcomes observed.

Contacts

Primary ContactHONGWEI JI
hongweijicn@gmail.com+8613120518791

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026